Low-carbon scheduling decision method for industrial park users considering production characteristics

KONG Xiangyu; YANG Zhenyu; LIU Ziyu; GAO Bixuan; ZHUANG Zhong; DUAN Meimei

doi:10.11930/j.issn.1004-9649.202503003

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Low-carbon scheduling decision method for industrial park users considering production characteristics

更新时间：2026-03-17

- Low-carbon scheduling decision method for industrial park users considering production characteristics
- Vol. 59, Issue 3, Pages: 37-47(2026)
- 作者机构：
  
  1. 天津大学智能电网教育部重点实验室，天津南开,300072
  2. 国网江苏省电力有限公司营销服务中心,江苏,南京,210019
- 作者简介：
- 基金信息：
- DOI：10.11930/j.issn.1004-9649.202503003
  CLC：
- Online First：16 March 2026，
  
  Published：16 March 2026
- 稿件说明：
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孔祥玉, 杨振宇, 刘子瑜, 高碧轩, 庄重, 段梅梅. 计及生产特性的工业园区用户低碳调度决策方法[J]. 中国电力, 2026, 59(3): 37-47.

KONG Xiangyu, YANG Zhenyu, LIU Ziyu, et al. Low-carbon scheduling decision method for industrial park users considering production characteristics[J]. 2026, 59(3): 37-47.
孔祥玉, 杨振宇, 刘子瑜, 高碧轩, 庄重, 段梅梅. 计及生产特性的工业园区用户低碳调度决策方法[J]. 中国电力, 2026, 59(3): 37-47. DOI： 10.11930/j.issn.1004-9649.202503003.

KONG Xiangyu, YANG Zhenyu, LIU Ziyu, et al. Low-carbon scheduling decision method for industrial park users considering production characteristics[J]. 2026, 59(3): 37-47. DOI： 10.11930/j.issn.1004-9649.202503003.

摘要

在“双碳”目标和新型电力系统建设的背景下，工业园区作为高耗能企业的集中地，其低碳清洁的运行方式是目前中国实现双碳目标中面临的重大挑战。针对工业园区用户低碳调控需求，提出了一种计及生产特性的低碳调度决策方法。首先建立了工业园区需求响应调控典型场景与业务流程，并考虑不同工业行业的生产工艺，提出面向不同时间尺度响应调控的工业用户分类及筛选方法，随后将碳减排收益纳入考虑，并以负荷聚合商收益最大为目标，构建需求响应低碳调度决策模型，并采用改进鲸鱼优化算法进行模型求解。最后通过实际算例，验证了该方法能够有效支撑工业园区负荷聚合商制定响应调控激励方案，在保证聚合商收益的同时减少园区碳排放量约10%。

Abstract

Under the "dual-carbon" goals and the construction of new power systems

industrial parks-as concentrations of energy-intensive enterprises-face significant challenges in achieving low-carbon and clean operation

which is critical for China to meet its national carbon targets. To address the low-carbon regulation requirements of industrial park users

a low-carbon scheduling decision method for industrial park users considering production characteristics is proposed. Firstly

we establish typical demand response regulation scenarios and operational frameworks for industrial parks. Considering the production processes of different industrial sectors

we develop an industrial user classification and screening method for multi-timescale response regulation. Subsequently

incorporating carbon emission reduction benefits into the optimization objectives

we formulate a demand response low-carbon scheduling decision model aiming at maximizing load aggregators' revenues. The model is solved using a modified whale optimization algorithm. Case studies demonstrate that the proposed method can effectively support load aggregators in formulating response regulation incentive schemes for industrial parks

achieving approximately 10% carbon emission reduction while maintaining aggregators' profitability.

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references

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